Identifying Prolonged Narcotics Users

World Drug Report by United Nations Office on
Drugs and Crimes in 2014 clearly suggests that during the period
2003-2012 the increase in crime rates for possession for personal
use worldwide was due to the increase in the total number of drug
users, esp. cannabis and ATS (Amphetamine-Type Stimulants).
Also with the recent improvements in the CCTV surveillance and
the introduction of wearable video cameras for police officers in
the United States and some other countries, a large amount of
data is available for biometric analysis.
We propose a system which can use the data of face images
from such sources and identify faces possibly altered by prolonged
narcotic drug usage. Experiments were conducted majorly on
before-after drug mug-shot images made public by Multinomah
Sheriff County Office. We use three different types of feature
extraction techniques: HoG, Local Binary Patterns and Color
Histogram, over which we apply a Support Vector Machine with
different kernels to classify the face images.